.TH "C:/Users/Matt/Documents/School/Robotics/zebulon/v2/vision/ext/SIFT/kdtree.h" 3 "13 Oct 2009" "Version 2" "zebulon" \" -*- nroff -*-
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.SH NAME
C:/Users/Matt/Documents/School/Robotics/zebulon/v2/vision/ext/SIFT/kdtree.h \- 
.SH SYNOPSIS
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.PP
\fC#include 'cxcore.h'\fP
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.SS "Classes"

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.ti -1c
.RI "struct \fBkd_node\fP"
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.SS "Functions"

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.RI "struct \fBkd_node\fP * \fBkdtree_build\fP (struct \fBfeature\fP *features, int n)"
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.RI "int \fBkdtree_bbf_knn\fP (struct \fBkd_node\fP *kd_root, struct \fBfeature\fP *feat, int k, struct \fBfeature\fP ***nbrs, int max_nn_chks)"
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.RI "int \fBkdtree_bbf_spatial_knn\fP (struct \fBkd_node\fP *kd_root, struct \fBfeature\fP *feat, int k, struct \fBfeature\fP ***nbrs, int max_nn_chks, CvRect rect, int model)"
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.RI "void \fBkdtree_release\fP (struct \fBkd_node\fP *kd_root)"
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.SH "Detailed Description"
.PP 
Functions and structures for maintaining a k-d tree database of image features.
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For more information, refer to:
.PP
Beis, J. S. and Lowe, D. G. Shape indexing using approximate nearest-neighbor search in high-dimensional spaces. In \fIConference on Computer Vision and Pattern Recognition (CVPR)\fP (2003), pp. 1000--1006.
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Copyright (C) 2006 Rob Hess <hess@eecs.oregonstate.edu>
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\fBVersion:\fP
.RS 4
1.1.1-20070913 
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.SH "Function Documentation"
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.SS "int kdtree_bbf_knn (struct \fBkd_node\fP * kd_root, struct \fBfeature\fP * feat, int k, struct \fBfeature\fP *** nbrs, int max_nn_chks)"Finds an image feature's approximate k nearest neighbors in a kd tree using Best Bin First search.
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\fBParameters:\fP
.RS 4
\fIkd_root\fP root of an image \fBfeature\fP kd tree 
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\fIfeat\fP image \fBfeature\fP for whose neighbors to search 
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\fIk\fP number of neighbors to find 
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\fInbrs\fP pointer to an array in which to store pointers to neighbors in order of increasing descriptor distance 
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\fImax_nn_chks\fP search is cut off after examining this many tree entries
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\fBReturns:\fP
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Returns the number of neighbors found and stored in \fInbrs\fP, or -1 on error. 
.RE
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.SS "int kdtree_bbf_spatial_knn (struct \fBkd_node\fP * kd_root, struct \fBfeature\fP * feat, int k, struct \fBfeature\fP *** nbrs, int max_nn_chks, CvRect rect, int model)"Finds an image feature's approximate k nearest neighbors within a specified spatial region in a kd tree using Best Bin First search.
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\fBParameters:\fP
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\fIkd_root\fP root of an image \fBfeature\fP kd tree 
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\fIfeat\fP image \fBfeature\fP for whose neighbors to search 
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\fIk\fP number of neighbors to find 
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\fInbrs\fP pointer to an array in which to store pointers to neighbors in order of increasing descriptor distance 
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\fImax_nn_chks\fP search is cut off after examining this many tree entries 
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\fIrect\fP rectangular region in which to search for neighbors 
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\fImodel\fP if true, spatial search is based on kdtree features' model locations; otherwise it is based on their image locations
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\fBReturns:\fP
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Returns the number of neighbors found and stored in \fInbrs\fP (in case \fIk\fP neighbors could not be found before examining \fImax_nn_checks\fP keypoint entries). 
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.SS "struct \fBkd_node\fP* kdtree_build (struct \fBfeature\fP * features, int n)\fC [read]\fP"A function to build a k-d tree database from keypoints in an array.
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\fBParameters:\fP
.RS 4
\fIfeatures\fP an array of features 
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\fIn\fP the number of features in \fIfeatures\fP 
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\fBReturns:\fP
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Returns the root of a kd tree built from \fIfeatures\fP. 
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.SS "void kdtree_release (struct \fBkd_node\fP * kd_root)"De-allocates memory held by a kd tree
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\fBParameters:\fP
.RS 4
\fIkd_root\fP pointer to the root of a kd tree 
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.SH "Author"
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